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Prediction of Percolation Threshold for Electrical Conductivity of CNT-Reinforced Cement Paste

CNT 보강 시멘트 페이스트의 전기전도에 관한 침투임계점 예측

  • Lee, Seon Yeol (Department of Civil and Environmental Engineering, Railway Infrastructure Research Center, Sejong University) ;
  • Kim, Dong Joo (Department of Civil and Environmental Engineering, Railway Infrastructure Research Center, Sejong University)
  • 이선열 (세종대학교 건설환경공학과 철도인프라 연구소) ;
  • 김동주 (세종대학교 건설환경공학과 철도인프라 연구소)
  • Received : 2022.08.30
  • Accepted : 2022.09.27
  • Published : 2022.09.30

Abstract

The percolation threshold of the CNT-reinforced cement paste is closely related to the optimal CNT amount to maximize the sensing ability of self-sensing concrete. However, the percolation threshold has various values depending on the cement, CNT, and water-to-cement ratio used. In this study, a percolation simulation model was proposed to predict the percolation threshold of the CNT-reinforced cement paste. The proposed model can simulate the percolation according to the amount of CNT using only the properties of CNT and cement, and for this, the concept of the number of aggregated CNT particles was used. The percolation simulation consists of forming a pre-hydrated cement paste model, random dispersion of CNTs, and percolation investigation. The simulation used CNT-reinforced cement paste with a water-cement ratio of 0.4 to 0.6, and the simulated percolation threshold point showed high accuracy with a simulation residual ratio of up to 7.5 % compared to the literature results.

CNT 보강 시멘트 페이스트의 침투 임계점은 자기감지 콘크리트의 감지 성능 극대화를 위한 최적 CNT 혼입량과 밀접한 관련이 있다. 하지만 침투임계점은 사용된 시멘트, CNT 그리고 물 시멘트 비에 따라 다양한 값을 가지며, 이를 얻기 위해서는 실험기반의 다수의 시행착오가 불가피하다. 이 연구는 CNT 보강 시멘트 페이스트의 침투 임계점 예측을 위한 침투 시뮬레이션 모델을 제안하였다. 제안된 모델은 CNT 그리고 시멘트 특성을 사용하여 CNT 혼입량에 따른 침투를 시뮬레이션할 수 있으며, 이를 위해 응집된 CNT 입자 수 개념이 사용되었다. 침투 시뮬레이션 과정은 미수화 시멘트 페이스트 모델 형성, CNT 랜덤분산, 그리고 침투 조사의 순서로 구성된다. 시뮬레이션에는 물 시멘트 비가 0.4-0.6인 CNT 보강 시멘트 페이스트가 사용되었으며, 시뮬레이션 된 침투 임계점은 문헌 결과 대비 최대 7.5 %의 시뮬레이션 잔차율로 높은 정확도를 보였다.

Keywords

Acknowledgement

본 연구는 국토교통부/국토교통과학기술진흥원의 지원으로 수행되었습니다(과제번호 22NANO-C156177-03).

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